System and method for determining vehicle price values

ABSTRACT

System and method for computing price values associated with vehicle history attributes including a vehicle history database with vehicle history records, a transaction database, a vehicle history knowledge determination module adapted to determine whether a sale transaction related to a transaction record occurred with buyer&#39;s knowledge of the vehicle history attributes associated with the vehicle of the transaction record and to classify each transaction record based on whether buyer possessed knowledge of the vehicle history attributes, and a price adjustment module adapted to determine price values for the vehicle history attributes based on the vehicle history datasets and classification of the plurality of transaction records. In accordance with one aspect, the present invention may be implemented as a computer readable medium with instructions.

CLAIM TO PRIORITY

This application is a divisional application of U.S. patent application Ser. No. 12/754,998 filed on Apr. 6, 2010 entitled “System And Method For Determining Vehicle Price Values”, which is a continuation-in-part application of U.S. patent application Ser. No. 12/543,918 filed on Aug. 18, 2009 entitled “System And Method For Determining Vehicle Price Adjustment Values”, which is a continuation application of U.S. application Ser. No. 10/721,780 filed on Nov. 26, 2003 entitled “System And Method For Determining Vehicle Price Adjustment Values”; the disclosures of which are hereby incorporated by reference as if set forth in full in this document for all purposes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to a system and method for determining price values for vehicles. In particular, the present invention is directed to such system and method in which price values for a vehicle are provided based on the vehicle history attributes.

2. Description of Related Art

The vehicle industry is perhaps one of the largest industries in many industrialized regions of the world. As a result, the market for used vehicles, and especially automobiles, has evolved into a substantial market, especially in North America, and in particular, the United States and Canada.

Those parties involved in the trading of used vehicles recognize the value of information relating to the used vehicle's history in making a purchasing decision including determining a value of the particular vehicle. Consequently, services have been created that function primarily to provide vehicle history information to various parties in the used vehicle market, including dealers and individual consumers. For example, Carfax, Inc., the assignee of the present invention, provides a variety of vehicle history information to both buyers and sellers of vehicles. Carfax offers a web site at www.carfax.com, permitting a potential buyer or seller of a vehicle to purchase a vehicle history report containing an extensive collection of information on a particular vehicle. Carfax also offers a subscription service for vehicle history reports. Additionally, dealers and other entities post links to vehicle history reports on various web sites, such as auto sales sites. The vehicle history report may include such vehicle history information as model year, odometer readings, as well as vehicle history events such as accident information, the existence of salvage and/or flood titles, and maintenance records.

Until now, commercial services which offer valuations of used motor vehicles have been limited to providing generic valuations, i.e. a base price, based primarily on the make, model and year of the vehicle. With the advent of online databases, the commercial online valuation services have been able to factor in various vehicle information relating to the present state of the specific vehicle, such as the color or odometer reading of the specific vehicle, or the zip code in which the vehicle is being sold, to allow adjustment to the generic base price.

For example, Edmunds'® TMV Used Vehicle Appraiser (www.edmunds.com) requests the mileage, color, zip code, specific standard/optional equipment, and vehicle condition from the user of its databases to make adjustments to the base price for the particular make, model and year of vehicle. These adjustments to the base price for Trade-In, Private Party, and Dealer Retail transactions are displayed on the pricing report to reach a final adjusted price.

Kelley Blue Book® (www.kbb.com) has a similar process which permits an owner to assess the vehicle's condition as poor, fair, good, or excellent, or permits the consumer to answer specific questions to assess the condition of the vehicle. Likewise, NADA Guides™ (www.nadaguides.com) also has a similar process which allows the consumer to input a zip code and mileage before submitting a request for a pricing report. The pricing report displays a price for Low Retail, Average Retail, and High Retail. Average Retail price is noted for representing the price for a vehicle having a clean vehicle history record. The report also displays a mileage adjustment for the mileage submitted by the consumer.

Other online services, such as Consumer Guide™ (www.consumerguide.com) that is powered by IntelliChoice®, provide information to dealers regarding potential customers in the market for a vehicle. After requesting the make, model, year, and standard/optional equipment, the valuation service requests the consumer to input fairly specific vehicle information regarding body damage, hail damage, amounts spent on unibody/frame and collision repairs, as well as data on emissions inspections, odometer reading, registration, and clean title/branded title(s). Consumer Guide™ also requests the consumer to answer questions about commercial use, purchase/sale at an auction, any Lemon Law claim, and any alterations made to the vehicle since it left the factory. Some of the requested information may be inputted manually by the consumer after reviewing a vehicle history report. Once these questions have been answered by the consumer, the online service provides such information to dealers who may be interested in taking the vehicle as a trade in for another vehicle from the dealer's inventory. Unlike the other online vehicle pricing services, however, the Consumer Guide™ pricing report does not display specific adjustments by dollar amount or display the value of the vehicle.

Importantly, the available databases and online valuation services described above do not disclose how their pricing adjustments are derived. For example, the above discussed online valuation services generally do not disclose how price values of a vehicle are determined for any given vehicle specific information. In this regard, some of the online valuation services specifically disclaim the accuracy of the valuation by stating that the vehicle valuation provided by the online valuation service is an opinion only.

Moreover, the presently available databases and online valuation services do not provide substantially automated prices or price adjustments for a used vehicle based on specific vehicle history information that relates only to the vehicle being valued. Instead, if such specific vehicle history information is used at all, the online valuation services rely upon the consumer to acquire and provide such information to the online valuation service. In particular, the above described attempts by the commercial vehicle valuation services to provide vehicle valuation services require interactively obtaining vehicle-specific data that is personally known to, or perceived by, the consumer. No vehicle valuation service substantially automatically obtains the breadth of information like the vehicle history data provided in the CARFAX® database. For example, as previously described, the Consumer Guide™ service requires the consumer to manually enter the vehicle history information that is requested.

Therefore, there exists an unfulfilled need for a system and method for determining vehicle prices or price adjustments. In addition, there also exists an unfulfilled need for such a system and method in which vehicle prices and price adjustments are provided based on vehicle history events. Furthermore, there exists an unfulfilled need for a system and method in which such vehicle prices and price adjustments are substantially automatically provided to the user without requiring the consumer to provide vehicle information.

SUMMARY OF THE INVENTION

The above described vehicle valuation providers have tried to provide model level vehicle valuations which vary from vehicle to vehicle by interactively soliciting vehicle specific information from the user, and factoring the obtained vehicle specific information into the calculation of the value of the vehicle. However, the actual value of used vehicles varies based upon market conditions and vehicle attributes such as the specifications of the vehicle, vehicle condition, or other circumstances pertinent to the particular vehicle involved in the transaction which may not be known by the user. As described more fully below, the present invention provides an effective system and method for calculating price values for vehicle history attributes more accurately by utilizing empirical data to derive the price or price adjustment. Correspondingly, the present invention solves the problems of the prior art and, in particular, allows more accurate determination of the value of a particular vehicle as compared to the prior art, with minimal user input.

In view of the foregoing, a system and method is disclosed for determining price of a vehicle. The price can be derived from a base price that is adjusted by a price adjustment determined from the vehicle history or the price can be directly or indirectly calculated.

The disclosed system calculates price values for vehicle history attributes based on actual transactions.

The disclosed system generates price values for a specific used vehicle accurately, with minimal user input.

The disclosed system generates accurate price values for a used vehicle substantially automatically based on vehicle history events specific to the used vehicle.

A method for determining price values is disclosed which can be used m implementing the system noted above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a general schematic illustration of a vehicle history information system in accordance with one embodiment of the present invention connected to various terminals and a distributed network.

FIG. 2 is a detailed schematic illustration of the vehicle history information system in accordance with one implementation of the present invention.

FIG. 3 shows a listing of various vehicle history attributes that may be provided in a vehicle history dataset stored in the vehicle history database.

FIG. 4 shows a listing of filter questions ordered in accordance with a filter logic in accordance with one embodiment of the present invention that may be used to determine whether a buyer of a vehicle had knowledge of vehicle history attributes.

FIG. 5 is a flow diagram illustrating one embodiment of the general method of the present invention.

FIG. 6 is a detailed flow diagram illustrating an example method of how the present invention may be used.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 is a schematic diagram of a system in accordance with one example embodiment of the present invention which can be used to determine price values for vehicles. Initially, it should be understood that the term “vehicle” is used broadly herein to encompass a variety of transportation devices. For example, vehicles include automobiles of all types, motorized cycles including motorcycles and all terrain vehicles, boats, watercraft, airplanes, etc. In this regard, the present invention may be implemented in the manner described to determine price values for such vehicles. Thus, although the example embodiment discussed in detail below focuses on automobiles, it should be understood that the present invention is not limited thereto but may be implemented to determine vehicle price values for any vehicle.

FIG. 1 is a schematic diagram of a system, for example in the form of a computer network 10, designed to implement one embodiment of the subject invention. FIG. 1 may also be viewed as showing the relationship of the different entities potentially involved in the application of the present invention. Specifically, a computer implemented vehicle history information system 12 exchanges data with a plurality of remote terminals 14 through data transmission across a distributed network 16, e.g. Internet. The terminals 14 represent an individual consumer and a vehicle dealer accessing vehicle history information system 12, as discussed more fully herein below, to obtain vehicle history information and/or vehicle price values for a particular vehicle.

Terminals 14 may also include one or more vehicle history data sources or suppliers that allow the vehicle history information system administrator to create and update vehicle history information in system 12. The vehicle history data supplier may be individual consumers, vehicle dealers, state titling offices, Department of Motor Vehicles, auto auctions and/or any other source of vehicle information. In addition, terminals 14 may further include transaction data source or supplier that provides transaction records having various information regarding a sale transaction of a particular vehicle to allow the system administrator to accurately determine price values in accordance with the present invention. Such transaction records may generally include identification of the vehicle that is the subject of the sale transaction (such as VIN), transaction date, and transaction amount. Transaction data supplier may be, for example, automobile dealers, manufacturers, or other data suppliers that collect transaction data regarding sale of vehicles.

The terminals 14 may be in communication with the vehicle history information system 12 via distributed network 16. The distributed network 16 may be any type of communications channel such as a local area network (LAN), wide area network (WAN), direct computer connections, and/or wireless connections using radio frequency, infrared, or other wireless technologies using any appropriate communication hardware and protocols, and may further be the Internet. Thus, terminals 14 may be connected to distributed network 16 by any communication links 18, including hardwired and/or wireless links. Moreover, the vehicle history data supplier and the transaction data supplier may provide information to vehicle history system administrator 12 via any means of effectively communicating vehicle history information, including conventional telephone, facsimile and/or mail services indicated at 20.

FIG. 2 illustrates in more detail the vehicle history information system 12 in accordance with one example embodiment of the present invention. Generally, vehicle history information system 12 may be implemented with any type of appropriate hardware and software, with portions of the system residing in the form of computer readable storage medium having executable instructions, and computer architecture as discussed herein below. Vehicle history information system 12 may be implemented using a server, personal computer, a portable computer, a thin client, etc. or any combination of such devices. In this regard, vehicle history information system 12 may be a single device at a single location as shown, or multiple devices at a single, or multiple, locations that are connected together using any appropriate communication protocols over any communication medium such as electric cable, fiber optic cable, any other cable, or in a wireless manner using radio frequency, infrared, or other technologies.

FIG. 2 also illustrates in more detail the preferred implementation of the terminals 14. Although only one terminal is shown in detail as the customer terminal which represents the individual consumers and vehicle dealers of FIG. 1, the details of the customer terminal 14 are equally applicable to the data supplier terminal and/or other terminals. Terminal 14 may be any appropriate device for accessing vehicle history information system 12 such as a personal computer, portable computer, thin client, a handheld device such as a mobile phone or PDA, and the like. Terminal 14 includes an input device 22 and an output device 24 which allow the user of the terminal 14 to provide information to, and receive information from, the vehicle history information system 12 via the distributed network 16. In this regard, the input device 22 may include a keyboard, mouse, etc. as well as data input devices such as memory devices based on magnetic, optical and/or solid state technologies including disc drives, CD/DVD drives, flash memory, etc. The output device 24 may include a monitor screen, printer, etc. that allow the user of the terminal 14 to obtain the vehicle history attribute information and/or price value information from vehicle history information system 12.

Referring again to FIG. 2, in the preferred embodiment, vehicle history information system 12 includes a price analysis unit 26, a price database 28, a vehicle history database 30, a transaction database 32, and a communications managing module 34, all of which are connected together for effective data communication. Price analysis unit 26 in the implementation shown includes a vehicle history report module 35, a vehicle history knowledge determination module 36, a price adjustment module 40, and a user interface module 42, the functions of each being further described herein below.

Vehicle history database 30 contains a plurality of vehicle history datasets which are comprehensive collections of vehicle history data arranged, organized, indexed and/or retrievable based on the unique vehicle identification number (such as VIN for automobiles) of a particular vehicle. Each vehicle sold within the United States and most foreign countries has a unique identification number which is identified on nearly every vehicle title issued and physically identified on the respective vehicle. The identification is the key to identifying and tracing the public record of each particular vehicle and to associating different vehicle data collected from a variety of sources with the particular vehicle.

It should be noted that the vehicle history information system 12 and the price analysis unit 26 in accordance with the embodiment of the present invention is illustrated and discussed herein as having various modules which perform particular functions. It should be understood that these modules are merely schematically illustrated based on their function for clarity purposes, and do not necessarily represent specific hardware or software. In this regard, these modules, units and other components may be hardware and/or software implemented on hardware to substantially perform their particular functions explained herein. The various functions of the different modules and units can be combined or segregated as hardware and/or software modules in any manner, and can be used separately or in combination. Thus, the present invention as schematically embodied in FIG. 2 should not be construed to limit the vehicle history information system 12 of the present invention, but be understood to be merely a schematic example.

It should be clarified that as used herein, the term “vehicle” generally refers to only one particular, physical vehicle associated with a single identification number and does not refer to general model level information or categories of vehicles. Such general model level information relating to a specific make, model and/or year, is referred to as “type” of vehicle herein. Thus, the vehicle history database 30 has a plurality of vehicle history datasets related to a plurality of vehicles, each vehicle history dataset being related to a particular vehicle and having vehicle history attributes regarding the vehicle as described below.

As previously mentioned, the administrator of vehicle history information system 12 acquires vehicle history datasets from a variety of data suppliers 14 including the vehicle history data supplier and the transaction data supplier. The vehicle history datasets from the vehicle history data supplier which are entered into vehicle history database 30 are associated with a particular identification number and thus, a particular vehicle. The vehicle data forming the vehicle history datasets are added as records to vehicle history database 30 and indexed by the identification number. The vehicle history datasets may be utilized by the vehicle history information system 12 in any appropriate manner. For example, the vehicle history datasets may be utilized by the vehicle history report module 35 to generate a report by retrieving vehicle history records associated with the requested identification number of a particular vehicle.

Vehicle history database 30 may be any appropriately implemented database capable of effectively storing collections of vehicle history data of vehicle history datasets in an organized accessible manner to permit efficient easy access to desired pieces of data, i.e. one or more records associated with a particular identification number, using appropriate database management system software. Preferably, vehicle history database 30 receives information from, and may be accessed by, various components of vehicle history information system 12.

The vehicle history datasets stored in the vehicle history database 30 preferably include vehicle history attributes which are vehicle history data that provide specific information which may affect a used vehicle's value in the market. Such vehicle history attributes may include accident information, such as salvage title, junk title, flood damage, fire damage, police accident report and damage disclosure information; mileage information, such as odometer problems and actual mileage listings; title/registration events including government registration, taxi registration and commercial registration; stolen vehicle information; fleet information; emissions and safety inspection information; recall information, number of owners, and any other information relevant to the vehicle's history and the vehicle's value. Such other information may be geographical location of the vehicle, color of the vehicle, etc., for example.

Additionally, whereas many of the above noted vehicle history attributes would generally reduce the value of the used vehicle, some of the vehicle history attributes may increase the used value of the vehicle as well, depending on the attribute. For example, a value of a used vehicle may be increased if the vehicle was used in a geographical region having a temperate climate. In another example, a value of a used vehicle may be increased if the number of previous owners is significantly lower than expected for the age of the vehicle. Of course, the converse may be true in that the value of the used vehicle may be reduced if the vehicle was used in a harsh geographical region or has a significantly higher number of previous owners than expected for the age of the vehicle.

In the above regard, FIG. 3 shows a listing of numerous vehicle history attributes that may be identified and stored in the vehicle history database 30 in a dataset associated with a particular vehicle. Most of the vehicle history attributes listed in FIG. 3 are self-explanatory. More specifically, “Air Bag Deployed” vehicle history attribute indicates that the particular vehicle's air bag has deployed which means that the particular vehicle was in an accident. “Abandoned” vehicle history attribute indicates that the particular vehicle was abandoned by an owner for some reason. “Accident” vehicle history attribute indicates that the particular vehicle was in an accident. “Assembled” vehicle history attribute indicates that the particular vehicle was assembled from a kit instead of being assembled at a factory of a vehicle manufacturer. “Auction Vehicle Offered” vehicle history attribute indicates that the particular vehicle was previously offered for sale at an auction whereas “Auction Vehicle Sold” vehicle history attribute indicates that the particular vehicle was actually sold.

“Child Lien Added” vehicle history attribute also listed in FIG. 3 indicates that a secondary lien has been added to the ownership rights to the particular vehicle. “Child Lien Removed” vehicle history attribute indicates that the secondary lien to the ownership rights to the particular vehicle has been removed. “Commercial Vehicle” vehicle history attribute indicates that the particular vehicle was registered for commercial use. “New Owner” vehicle history attribute indicates that the ownership for the particular vehicle changed from one party to another. “Cert. Pre-Owned Vehicle” vehicle history attribute indicates that the particular vehicle is a certified used vehicle which has been inspected and determined by, for example, the dealer, to meet the requirements established by, for example, the manufacturer for certification that provides indication of condition and reliability of the particular vehicle.

“Curbstoning” vehicle history attribute indicates that the particular vehicle was sold by a dealer posing as a private owner of the vehicle, such sale tactics being generally employed when there is something wrong with the vehicle and the dealer wants to deceive the buyer and not disclose the problems of the vehicle. “Crash Test Vehicle” vehicle history attribute indicates that the particular vehicle was used in vehicle crash testing. “Damaged by Fire” vehicle history attribute indicates that the particular vehicle, at one time, was on fire. “Damage Disclosure” vehicle history attribute indicates that damage to the particular vehicle was reported, for example, by the owner. “Dismantled Title” vehicle history attribute indicates that the particular vehicle was dismantled for parts or recycling, for example, by a salvage yard. “Emissions Inspection Failed” vehicle history attribute indicates that the particular vehicle failed emissions testing while “Emissions Inspection Passed” vehicle history attribute indicates that the particular vehicle passed emission testing.

“First Owner” vehicle history attribute indicates that the indicated registrant of the particular vehicle is the first owner. “Fire Damaged” vehicle history attribute indicates that the particular vehicle was damaged in a fire whereas “Flood Damaged” vehicle history attribute indicates that the particular vehicle was damaged due to water. “Fleet Vehicle” vehicle history attribute indicates that the particular vehicle was used in a commercial fleet, for example, as a company vehicle. “Grey Market” vehicle history attribute indicates that the particular vehicle was originally manufactured for use in another country but was imported by a dealer, and the manufacturer's warranty may be void. “Gross Polluter” vehicle history attribute indicates that the particular vehicle has emissions problems and does not meet emission requirements. “Government Vehicle” vehicle history attribute indicates that the particular vehicle was registered for use by a government agency which may indicate severe usage. “Hail Damaged” vehicle history attribute indicates that the particular vehicle has incurred body damage caused by hail. “Junk Title” vehicle history attribute indicates that the particular vehicle is no longer suitable for use on public roads.

“Lien 1 Reported” vehicle history attribute indicates that a lien has been placed on the rights to the ownership of the particular vehicle, for example, by a bank, while “Lien 2 Reported” vehicle history attribute indicates that a second lien has been placed on the rights to the ownership of the particular vehicle, for example, a lien from another bank or a mechanic's lien. “Lease Vehicle” vehicle history attribute indicates that the particular vehicle was leased instead of purchased. “Lemon Law Vehicle” vehicle history attribute indicates that the particular vehicle was defective at the time of manufacture and the manufacturer was forced to re-purchase the particular vehicle back from the owner under state law. “Not Actual Mileage” vehicle history attribute indicates that the particular vehicle's odometer does not show the actual mileage of the vehicle. “Non-Branded Buyback” vehicle history attribute indicates that the particular vehicle was purchased back by the manufacturer without establishing that the particular vehicle was a lemon under the lemon law, thus, not requiring a lemon law vehicle history attribute.

“Number of Owners” vehicle history attribute indicates that the particular vehicle has had the indicated number of owners. “Non-legal High Way” vehicle history attribute indicates that the particular vehicle cannot be used on public roads, for example, if the particular vehicle is a race car. “Non Profit” vehicle history attribute indicates that the particular vehicle was registered or owned by a not for profit organization. “Odometer Actual” vehicle history attribute indicates that the particular vehicle's odometer shows the actual mileage of the particular vehicle while “Odometer Rollback” vehicle history attribute indicates that the odometer has potentially been tampered with to show a mileage less than the actual mileage of the particular vehicle. “Odometer Rollover” vehicle history attribute indicates that the mileage of the particular vehicle exceeded the five digit mechanical limits of the vehicle's odometer, i.e. beyond 99,999 miles, causing the odometer to be reset to zero.

“Police Vehicle” vehicle history attribute indicates that the particular vehicle was registered for police use which suggests severe usage. “Private Vehicle” vehicle history attribute indicates that the particular vehicle was registered to a private individual. “Recall” vehicle history attribute indicates that the particular vehicle was subject to a recall by the government and/or manufacturer. “Rebuilt Vehicle Title” vehicle history attribute indicates that the particular vehicle was in a serious accident but was rebuilt to be suitable for use. “Rental Vehicle” vehicle history attribute indicates that the particular vehicle was registered for use in a rental fleet which suggests severe usage. “Reported Stolen” vehicle history attribute indicates that the particular vehicle was reported to a law enforcement agency as being stolen. “Reported Veh. Damage” vehicle history attribute is the same as the “Damage Disclosure” vehicle history attribute and indicates that damage has been reported for the particular vehicle, for example, by the owner. “Repossessed” vehicle history attribute indicates that the particular vehicle was recovered by a party such as a bank that has ownership rights to the vehicle. “Safety Insp. Failed” vehicle history attribute indicates that the particular vehicle failed an inspection required in many jurisdictions, and by some insurance companies, to ensure road worthiness of vehicles whereas “Safety Insp. Passed” vehicle history attribute indicates that the particular vehicle passed such an inspection.

“Salvaged Title” vehicle history attribute indicates that the particular vehicle was in a severe accident and was repurchased by the insurance company. “Taxi Vehicle” vehicle history attribute indicates that the particular vehicle was registered for use as a taxi. “Theft Recovery” vehicle history attribute indicates that the particular vehicle was stolen, but then was recovered. “Title Issued” vehicle history attribute indicates that a title was issued by an authorized agency for the particular vehicle, and similarly, “Title/Regist. Issued” vehicle history attribute indicates that a title registration was issued for the particular vehicle. “Total Loss” vehicle history attribute is similar to “Salvage Title” vehicle history attribute and indicates that the particular vehicle was in an accident and that the total value of the vehicle was paid to the insured by the insurance company that insured the vehicle, rather than paying for the repair of the vehicle. “Vehicle Repaired” vehicle history attribute indicates that the particular vehicle had a particular problem which was repaired. “Vehicle Serviced” vehicle history attribute indicates that the particular vehicle was serviced for routine maintenance.

Of course, the vehicle history attributes that are listed in FIG. 3 and discussed above are merely examples and other vehicle history attributes may also be stored in the vehicle history database 30 as well. Preferably, the vehicle history attributes are any data that may affect the price of the used vehicle. This information may be retrieved, processed and/or displayed in any desired manner, for example, retrieved and processed by the vehicle history report module 35 to create corresponding vehicle history reports for a particular VIN that can be displayed by the user interface module 42.

In a similar manner, the transaction database 32 may be any conventional database capable of effectively storing collections of sale transaction records in an organized accessible manner using appropriate database management system software which allows efficient and easy access to desired pieces of data. As noted, transaction records may generally include identification of the used vehicle that is the subject of the sale transaction such as VIN for automobiles, as well as specific information regarding the transaction such as the transaction date, and transaction amount. The transaction database 32 allows one or more transaction records associated with a particular identification number to be retrieved from the transaction database 32. As previously noted, sale transaction information may be provided by a data supplier such as automobile dealers, manufacturers, or other data suppliers. Of course, whereas in the present invention, the transaction database 32 and vehicle history database 30 are illustrated as separate databases, it should be evident that in other embodiments of the present invention these databases may be combined into an integral database having both the vehicle history datasets and the transaction records therein. While the transaction records of the disclosed embodiment are sales transaction records, the transactions records could be any vehicle transaction record, such as a vehicle listing, an offer to purchase a vehicle or a consumer survey record. Price values can be determined based on consumer or other market surveys. For example, a sample of people could be asked how much they would value a vehicle having specific history attributes. The survey answers can be used to build a pricing model. Note that the price values can be any value indicative of a price, such as an adjustment from a base price, a price component, or a percentage adjustment. Surveys are consumer research relating to the value of a vehicle history attribute. For example, a survey can ask consumers “what would you pay for a vehicle with a specific history attribute or bundle of attributes?” Alternatively the survey could ask “what did you pay for a vehicle with a specific history attribute or bundle of attributes?”

Accordingly, the transaction price can be any price related to a vehicle transaction, such as a purchase offer price, a listing, an auction price or a sales asking price.

The vehicle history information system 12 in accordance with the illustrated embodiment of FIG. 2 further includes price database 28 adapted to store calculated price values for vehicle history attributes, examples of vehicle history attributes being listed in FIG. 3 as previously noted. The price values are each indicative of a specific vehicle history attribute's impact or effect on the value of the particular used vehicle. For example, if a particular vehicle has been in a major accident or has a salvage title, such vehicle history attributes can be expected to lower the value of the particular used vehicle by a certain amount. By providing price values that correspond to the vehicle history attributes, the value of the particular used vehicle may be accurately adjusted to reflect such vehicle history attributes, such attributes being provided by the vehicle history database 30. The price values may be expressed in any appropriate manner such as a fixed amount, a percent of the value of the vehicle, or using another mathematical formula or model. As will be explained in further detail below, such price values are calculated using the available vehicle history data from the vehicle history database 30 and the transaction records from the transaction database 32. In the preferred embodiment shown, a price analysis unit 26 includes appropriate hardware and software for implementing the vehicle history report module 35, the vehicle history knowledge determination module 36, the price adjustment module 40, and the user interface module 42, each module performing the functions as described in detail below. In this regard, price analysis unit 26 may be implemented as a general purpose computing device with a central processing unit (CPU) or processor. The software for operating the price analysis unit 26 and the various modules may reside m a computer readable storage medium in the form of encoded executable instructions that operate the vehicle history information system 12 and perform the functionalities and process steps described.

In particular, the vehicle history report module 35 functions to access vehicle history database 30 to retrieve appropriate vehicle history records associated, for example, with a particular VIN that is requested by a user of the vehicle history information system 12. Thus, the vehicle history module 35 includes the appropriate software necessary to identify the appropriate vehicle history dataset from the vehicle history database 30, and to retrieve vehicle history data based on a particular request, for example, based on a particular VIN. The vehicle history report module 35 may further be adapted to arrange and organize the vehicle history data and information in a manner appropriate for further data processing and/or display as a vehicle history report via the user interface module 42 described below.

User interface module 42 is adapted to generate a user interface or output for delivery to output device 24 of customer terminal 14. In particular, the user interface module 42 may be adapted to generate particular electronically displayable files for delivery to, and display by, output device 24 of customer terminal 14. For example, the user interface module 42 may utilize the information provided by the vehicle history report module 35 and/or price adjustment module 40 described in further detail below to generate an output which is provided to the output device 24 of terminal 14. Communications managing module 34 is adapted to manage communications and interactions between vehicle history information system 12 and its various components, as well as with the various terminals 14 via the distributed network 16.

As noted above, the price values that are retrievably stored in the price database 28 are calculated using the available vehicle history datasets from the vehicle history database 30 and the transaction records from the transaction database 32. This is attained by the vehicle history knowledge determination module 36 and the price adjustment module 40 of the price analysis unit 26 in accordance with the illustrated embodiment, the details of these modules being described below.

The vehicle history knowledge determination module 36 of the price analysis unit 26 is adapted to determine buyer's knowledge of one or more vehicle history attributes for the particular vehicle of a transaction. More specifically, the illustrated embodiment of the vehicle history knowledge determination module 36 is adapted to determine whether sale transactions related to the transaction records stored in the transaction database 32 occurred, or likely occurred within a particular degree of certainty, with the buyer's knowledge of one or more of the vehicle history attributes associated with the vehicle of the transaction record being analyzed.

In the above regard, the present application generally refers to buyer's knowledge, or utilizes the phrase “buyer knew” in discussing whether a sale transaction related to a transaction record occurred with buyer's knowledge of at least one vehicle history attribute. As discussed, it should be understood that such references to buyer's knowledge of at least one vehicle history attribute includes actual knowledge, as well as a determination of buyer's knowledge by inference within a specific degree of certainty.

In addition, in the illustrated embodiment discussed, the vehicle history knowledge determination module 36 can also determine whether sale transactions or other transactions related to the transaction records occurred, or likely occurred within a particular degree of certainty, without the buyer's knowledge of one or more of the vehicle history attributes. In this regard, the present application generally refers to buyer's lack of knowledge, or utilizes the phrase “buyer did not know” in discussing whether a sale transaction related to a transaction record occurred with buyer's knowledge of at least one vehicle history attribute. Again, it should be understood that such references to buyer's lack of knowledge of at least one vehicle history attribute includes actual lack of knowledge, as well as a determination of buyer's lack of knowledge by inference within a particular degree of certainty. Moreover, the vehicle history knowledge determination module can also determine if the knowledge of the buyer during the sale transaction cannot be determined for whatever reason.

The determination as to buyer's knowledge (actual or inferred) of one or more of the vehicle history attributes may be attained in any appropriate manner, such as based on one or more filter questions which are analytical tools that may be used by the vehicle history knowledge determination module 36. For instance, this determination may be based on buyer's interaction with the vehicle history database 30 of the vehicle history information system 12 via the user interface module 42 regarding the particular vehicle being analyzed. More specifically, buyer's interaction may include the buyer's action of requesting a vehicle history report from the vehicle history report module 35 for the used vehicle being analyzed via the user interface module 42. The buyer's interaction may alternatively be acknowledgement that a vehicle history report for the vehicle being analyzed was used in a sale transaction relating to sale of the particular vehicle being analyzed. The interaction may also be registration of the particular vehicle in a title guarantee associated with the vehicle history information system 12 and/or provision of comments regarding the particular vehicle being analyzed.

All of these interactions can be determined using filter questions, whereby answers to such filter questions provide an indication or inference as to whether the buyer of the particular used vehicle for the transaction record being analyzed knew of the vehicle history attributes, or that the buyer did not know of the vehicle history attributes. Of course, the above noted interactions of the buyer with the vehicle history information system 12 are merely examples of interactions that can be used by the vehicle history knowledge determination module 36 in determining buyer's knowledge of the vehicle history attributes.

Alternatively, knowledge of the buyer as to one or more of the vehicle history attributes may be determined by the vehicle history knowledge determination module 36 based on access data indicating access to the vehicle history dataset in the vehicle history database 30 which relate to the vehicle being analyzed, and based on a transactional record relating to the vehicle being analyzed in the transaction database 32. More specifically, the vehicle history knowledge determination module 36 may be adapted to determine whether the identity of a user who accessed the vehicle history dataset in the vehicle history database 30 regarding the particular vehicle is also identified as a purchaser or buyer in a transaction record relating to sale transaction of the vehicle being analyzed. A match in the identity provides an indication that the buyer likely had knowledge of the vehicle history attributes. Again, this determination may be made using filter questions or in any other appropriate manner.

Of course, other techniques may alternatively be used to determine whether a sale transaction occurred with/without buyer's knowledge of the particular vehicle's vehicle history attributes. For example, the vehicle history knowledge determination module 36 may determine buyer's knowledge based on filter questions that provide specific knowledge or an indication as to the likelihood that the seller of the used vehicle provided prospective purchasers with a vehicle history report generated by the vehicle history report module 35, or the information on such a report.

In the above regard, the vehicle history knowledge determination module 36 may implement a filter logic as set forth in FIG. 4 in which answers to a series of filter questions serve as a basis for determining buyer's knowledge of the particular vehicle's vehicle history attribute at the time of the sale transaction of a transaction record. For example, filter question A of FIG. 4 queries whether a consumer of Carfax® ran a vehicle history report on the VIN being analyzed. If the answer is yes, filter question B is queried in which a determination is made as to whether there is some buyer identification available in the transaction record that is related to the sale transaction of the VIN. If so, then filter question C is queried, and so forth.

It should be understood that the filter questions referred to above and samples of which are set forth in FIG. 4 need not be actual questions that are posed to a user of the vehicle history information system 12. Rather, these filter questions represent factual inquiries for which answers can be provided directly by the user of the vehicle history information system 12, or derived inferentially by the vehicle history knowledge determination module 36 itself or an administrator of the system based on other facts or information available to the vehicle history information system 12. For example, such information may be access data noted above that indicates access to the vehicle history dataset in the vehicle history database 30.

As described, the filtration questions implemented using the filter logic as set forth in FIG. 4 allow the vehicle history knowledge determination module 36 to determine whether sale transactions related to the transaction records stored in the transaction database 32 occurred with buyer's knowledge of the vehicle history attribute(s) associated with the particular vehicle of the transaction record being analyzed. The filtration questions also allow determination of whether the sale transactions occurred without buyer's knowledge of the vehicle history attribute(s), or if buyer's knowledge of the vehicle history attribute(s) cannot be determined.

In addition, as can be appreciated by careful examination, the filtration questions also allow inferential determination of the buyer's knowledge based on behavior of the seller and/or buyer identified in the transaction record when specific confirming information is not readily available or determinable. For example, in filtration question S, the fact that the dealer selling the vehicle generally provides vehicle history reports to prospective customers is used to infer that the buyer of the vehicle did have knowledge of the vehicle history attributes associated with the particular vehicle. Furthermore, it should also be noted that FIG. 4 provides filter questions that are specific to Carfax® vehicle history services. However, similar filter questions or alternative filter questions and/or filter logic may be utilized to determine the buyer's knowledge regarding the vehicle history attributes.

Moreover, the answers to the filter questions as set forth in FIG. 4 allow the vehicle history knowledge determination module 36 of the price analysis unit 26 to classify each transaction record based on the buyer's knowledge, or lack thereof, regarding the vehicle history attributes associated with the particular vehicle. In the present embodiment, the vehicle history knowledge determination module 36 is also adapted to classify the transaction records of the transaction database 32 into classes including a “Known Class” in which sale transaction occurred with buyer's knowledge of the vehicle history attributes, a “Not Known Class” in which sale transaction occurred without buyer's knowledge of the vehicle history attributes, and an “Uncertain Class” where the buyer's knowledge of the vehicle history attributes is indeterminate. Thus, the “Known Class” would contain transaction records in which the sale transactions occurred, or likely occurred, with buyer's knowledge of the vehicle attributes whereas the “Not Known Class” would contain transaction records in which the sale transactions occurred, or likely occurred, without buyer's knowledge of the vehicle attributes.

For example, referring again to the filter questions set forth in FIG. 4, if a transaction record satisfies filter question C, the transaction record is determined by the vehicle history knowledge determination module 36 to be in the Known Class which is designated as “KC” in FIG. 4. If the sale transaction of the transaction record is determined to have occurred without buyer's knowledge of the vehicle history attributes for filter question H, the transaction record is determined to be in the Not Known Class by the vehicle history knowledge determination module 36 which is designated as “NKC” in FIG. 4. However, if the buyer's knowledge of the vehicle history attributes are indeterminate, for example, in filter question T, the transaction record is determined to be in the Uncertain Class by the vehicle history knowledge determination module 36 which is designated as “UC” in FIG. 4.

As described in further detail below, the price adjustment module 40 of the price analysis unit 26 is adapted to determine price values for the vehicle history attributes based on the vehicle history datasets stored in the vehicle history database 30 and classification of the plurality of transaction records in the transaction database 32 as attained by the vehicle history knowledge determination module 36 in the manner described above, for example. The price adjustment module 40 determines the price values for the vehicle history attributes such as those listed in FIG. 3 based on transaction records that are in the Known Class, as well as the transaction records that are in the Not Known Class, transaction records in the Uncertain Class not being used in the present embodiment.

The price adjustment module 40 of the illustrated embodiment is adapted to process the plurality of transaction records that are classified by the vehicle history knowledge determination module 36 in the Not Known Class. For example, the price adjustment module 40 of the illustrated embodiment may be adapted to determine an average transaction price from the transaction records in the Not Known Class for vehicles having the same, or similar, parameters. In this regard, the average transaction price for a type of vehicle may be calculated, i.e., for vehicles having the same make, model and year. This provides a baseline price for a type of vehicle when the vehicle history attributes are not known. It should be understood that as used herein, the term “vehicle parameter” refers to any information or data that may be used to categorize or group the transaction records that are used by the price adjustment module 40 for its processing. Thus, such vehicle parameters may include the type of vehicle as noted (make, model and year) but may further include other parameters such as mileage, color, location, options, or even any of the vehicle history attributes referred to in FIG. 3.

Of course, in alternative implementations, the price adjustment module 40 may be adapted to process the plurality of transaction records to determine other values, for example, mean transaction price. In addition, various other vehicle parameters may also be used by the price adjustment module 40 to categorize or create a focused group of transaction records related to vehicles having one or more similar, or the same, vehicle parameters.

In accordance with the preferred embodiment, the price adjustment module 40 is further adapted to identify the transaction records for vehicles classified in the Known Class that have the same, or similar, vehicle parameters. Using these identified transaction records, the price adjustment module 40 calculates a price differential between the transaction price associated with a transaction record in the Known Class, and the average transaction price that was determined using transaction records in the Not Known Class. In this regard, a plurality of price differentials are calculated from the transaction records in the Known Class so as to provide higher level of accuracy and confidence in the price values that are derived therefrom.

The price adjustment module 40 then determines the price values by attributing the calculated price differential to the vehicle history attributes for the used vehicles of the transaction records in the Known Class. Thus, the price values are determined based on the presumption that the differences in prices between used vehicles for which vehicle history attributes are known/likely to be known, and used vehicles for which vehicle history attributes are not known/likely to be not known, are directly attributable to the specific vehicle history attributes identified in the vehicle history datasets of the particular vehicles in which the vehicle history attributes are known by the buyers. In particular, the vehicle history attributes for each used vehicle may be mathematically modeled as an equation. For example, the values of the vehicle history attributes may be expressed as variables which are multiplied with the price values which may be expressed as unknown constants of the equations, the sum of the products of the variables and constants for each used vehicle equaling the determined price differential for each vehicle. This results in a plurality of equations relating to each type of vehicle, each equation having a plurality of constants, i.e. price values, that can be calculated by the price adjustment module 40 by apportioning the price differential to the vehicle history attributes.

In the preferred embodiment, multivariate analysis may be performed in which the plurality of equations are solved simultaneously to calculate the price values, i.e. the unknown constants of the equations. Such mathematical techniques for solving for multiple variables are known in the field of mathematics and thus, are not discussed in detail here. Thus, the price values are calculated using empirical data based on the actual transaction records such that the price values accurately reflect the impact of the vehicle history attributes on the market value of a used vehicle. As an example, the multivariate analysis can be regression analysis. The analysis can include a first stage of performing multivariate analysis on a collection of vehicle history attributes for various types of vehicles, and a second stage of performing multivariate analysis on a collection of vehicle history attributes for a specific type of vehicle.

The calculated price values are then retrievably stored in the price database 28 for use in any appropriate manner. For example, the price values may be displayed by the user interface module 42 of the vehicle history information system 12 to a user requesting information about a particular vehicle in conjunction with a vehicle history report generated by the vehicle history report module 35. Only the price values that are relevant for the vehicle, i.e. those corresponding to vehicle history attributes present in the vehicle history report, may be displayed so that the user of the vehicle history information system 12 can take into consideration the impact of vehicle history attributes on the actual value of the particular vehicle. Alternatively, an actual base price of the vehicle may be obtained and adjusted using the calculated price values in the manner further described below to thereby provide accurate valuation of a particular vehicle based on its history. When another user requests information about another used vehicle, the price values that are relevant for the vehicle can be retrieved from the price database 28 and displayed so that adjustments to the price of the used vehicle can be made.

In the above described implementation, to enhance the accuracy of the price values calculated, the transaction records classified in the Not Known Class and the Known Class may be windowed by the pricing adjustment module 40 or the vehicle history knowledge determination module 36 so that only transaction records having a transaction date within a predetermined time window is used to determine the average transaction price. Moreover, the price values stored in the price database 28 can be periodically updated in accordance with the method described above. This will ensure that the transaction records used to determine the price values are reflective of current market conditions and are not skewed by transaction records that are outdated and no longer accurately reflect present market conditions. The window may be one year or even shorter such as six months so that only the sale transactions that occurred within the time window are used.

Moreover, it should be noted that, preferably, the mathematically modeled equations from which price differentials are determined by the price adjustment module 40 is for the same type of vehicle having the same vehicle parameters. This would provide accurate price values for a particular vehicle type, for example, price values for 1999 Honda Accords. Of course, in alternative embodiments, price values that are generic to all types of vehicles may be calculated by determining the price values at an aggregate level using mathematically modeled equations for a variety of different types of vehicles or based on other vehicle parameters. Such an alternative embodiment may be desirable, for example, if there are not enough transaction records for one type of vehicle to determine statistically accurate price values, for example, for exotic sports cars.

As noted above, in accordance with another example implementation, the price adjustment module 40 may further be adapted to provide valuation for a used vehicle using the price values that are retrievably stored in the price database 28. In particular, a base price for a used vehicle may be obtained and adjusted using the price values based on the vehicle history attributes set forth in the vehicle history dataset related to the particular used vehicle. The base price information of vehicles may be obtained from various third party databases and online services that provide such pricing information. For example, base price for the particular type of vehicle may be obtained from third party pricing databases such as Kelley Blue Book® (www.kbb.com), NADA Guides™ (www.nadaguides.com), Edmunds® (www.edmunds.com), or any other appropriate sources. As previously described, the base price of vehicles as provided by such third party databases represent general used car prices for a particular type of vehicle which takes into consideration the year, make, model and mileage of the vehicle, as well as some other general information such as options. However, such databases do not have vehicle history datasets associated with particular vehicles that set forth detailed information regarding vehicle history attributes which will effect vehicle values in the marketplace.

Hence, it should now be evident that the present invention allows facilitated price determinations for each of the vehicle history attributes, or a combination thereof, associated with the used vehicle using the price values calculated as described herein to provide vehicle valuation that more accurately reflects the actual value of the used vehicle. Thus, in such an implementation, the determined price of the vehicle itself may be displayed by the user interface module 42 to the user of the vehicle history information system 12. Moreover, such price values and price determinations are provided in a substantially automated manner with minimal input from the user of the vehicle history information system 12.

In view of the above, it should be evident that another aspect of the present invention is a method for generating price values. In this regard, FIG. 5 shows flow diagram 50 that illustrates the general method for determining price values associated with the vehicle attributes in accordance with one embodiment. As shown, vehicle history datasets relating to a plurality of vehicles are obtained in step 51, and transaction records relating to sale transactions of the plurality of vehicles are obtained in step 52. Such data may be obtained from the vehicle history database 30 and the transaction database 32 of FIG. 2, for example.

In step 54 of flow diagram 50, a determination is made as to whether each of the sale transactions occurred with buyer's knowledge (actual or inferred) of the vehicle history attributes associated with the vehicle of the transaction record. This step may be attained using filter questions 53 and filter logic discussed previously relative to FIG. 4. The transaction records are then classified in step 55 based on the buyer's knowledge of vehicle history attributes as determined in step 54. For example, the transaction records may be classified into a Known Class in which the buyer possessed knowledge of the vehicle history attributes, Not Known Class in which the buyer did not possess knowledge of the vehicle history attributes, and Uncertain Class in which buyer's knowledge of the vehicle history attributes, or lack thereof, cannot be determined with sufficient certainty.

The price values for the vehicle history attributes are then determined based on the classification of the transaction records in step 59. In particular, price differentials between the prices associated with transaction records in the Known Class and average price from transaction records in the Not Known Class are calculated in step 56. The vehicle history attributes of each vehicle are then mathematically modeled in step 57 to equal the determined price differential. More specifically, price differentials are set equal to the sum of the products of the vehicle history attributes and price values, thereby resulting in a plurality of equations. Multivariate analysis is then performed in step 58 to simultaneously solve the plurality of equations to solve for the unknown constants which correspond to the price values for the vehicle history attributes. Of course, the method as described in flow diagram 50 of FIG. 5 is merely one example method of the present invention and it should be evident to one of ordinary skill in the art that the method may be readily modified or changed.

For clarity, FIG. 6 illustrates a detailed flow diagram 60 which provides an example of how the present invention in accordance with the method of FIG. 5 discussed above may be used. In particular, in step 62, vehicle history datasets and transaction records are obtained, for example, from vehicle history database 30 and transaction database 32 shown in FIG. 2. Such information preferably includes the VIN, selling price of the vehicle, mileage, transaction date, and optionally, a buyer identifier and transaction location, as well as other information. The vehicle history datasets further include vehicle history attributes regarding a particular vehicle, for example, one or more of the vehicle history attributes listed in TABLE 1 of FIG. 3. In step 64, each sale transaction associated with a transaction record is analyzed to determine buyer's knowledge of one or more of the vehicle history attributes for the particular vehicle being analyzed. This step may be performed in the present implementation by the vehicle history knowledge determination module 36 as discussed previously utilizing the filter questions and logic set forth in FIG. 4.

The transaction records are then classified, for example, by the vehicle history knowledge determination module 36, based on the buyer's knowledge of the vehicle history attributes as determined in step 64. Data matrix 66 shows several examples of transaction records and vehicle history attributes from a portion of vehicle history dataset in the Not Known Class in which the buyer did not know of the vehicle history attributes. Of course, it should be evident that the data matrix 66 only shows information for three vehicles, namely, three 1998 Ford Taurus vehicles, and only several of the vehicle history attributes. In actuality, the data matrix 66 would include expanded data with more than three vehicles to thereby provide a larger data pool.

Average transaction price for the type of vehicle, in this case, 1998 Ford Taurus, is computed in step 68 for the transaction records in the Not Known Class. Various other vehicle parameters in addition to make, model and year may be taken into consideration in determining whether a transaction record for a particular vehicle should be used in calculating the average transaction price. For example, mileage may be taken into consideration so that an average transaction price is provided for a particular mileage range of the type of vehicle, for example, 0-25K miles, 26-50K miles, 51-75K miles, etc. Thus, in accordance with the illustrated implementation, the average transaction price is computed for each type of vehicle and the mileage ranges. Of course, additional vehicle parameters to those shown in the data matrix 66 may also be taken into consideration in determining whether a transaction record for a particular vehicle should be used in calculating the average transaction price as well.

The computed average transaction price as determined in step 68 is used as a basis for determining the price values in conjunction with the transaction records and vehicle history datasets for the same type of vehicle in the Known Class as generally shown in the analysis matrix 70 of FIG. 6. In particular, the same type of vehicles in the Known Class are identified and the vehicle history datasets with the vehicle history attributes associated with the identified vehicles are retrieved, for example, from the vehicle history database 30. Again, other computed transaction price may be used, for example, mean transaction price. In addition, the transaction records regarding the identified vehicles in the Known Class are retrieved from, for example, the transaction database 32, and matched with the vehicle history attributes. It should be noted that in the analysis matrix 70, information for only three vehicles are shown and only several of the vehicle history attributes for these vehicles are shown for simplicity and to facilitate the understanding of this aspect of the present embodiment. However, in actual implementation, more vehicles with corresponding vehicle history datasets and vehicle history attributes would be used for analysis.

In step 72, a price differential between the price associated with each transaction record in the Known Class, and the computed average price from transaction records in the Not Known Class for the same type of vehicle, are calculated. More specifically, the price differentials between the “Vehicle Selling Price” column of the analysis matrix 70 and the “Avg Price When History Not Known” column of the analysis matrix 70 are determined. Of course, the above data matrix 66 and the analysis matrix 70 are illustrated in FIG. 6 to merely facilitate understanding of the present method of calculating the price values and do not represent a specific data structure or data format required to practice the present invention.

In step 74, the vehicle history attributes for each vehicle are mathematically modeled and set equal to the calculated price differential of step 72. Thus, as shown:

Price differential=aw+bx+cy+dz . . . etc.

where w, x, y, z, etc. are variables corresponding to values of vehicle history attributes; and

where a, b, c, d, etc. are constants corresponding to price values for the vehicle history attributes.

Step 74 results in a plurality of unsolved equations corresponding to each of the vehicles in the Known Class that are being analyzed. Most of the vehicles are likely to have differing vehicle history attributes and price differentials. Thus, the variables w, x, y, z etc. will likely be different for each of the vehicles.

Some vehicle history attributes are already numeric in nature, for example, the number of owners. Other vehicle history attributes may just be yes/no type of attributes, for example, a lemon title designation in the vehicle history dataset. Such vehicle history attributes may be assigned numeric values so that mathematical modeling and analysis may be performed. For instance, no lemon vehicle designation may be assigned a value “0” while a yes lemon vehicle designation may be assigned a value “1”. Again, it should be noted that only several of the vehicle history attributes are modeled in the above example for simplicity in understanding the method of the present invention. However, the present invention is preferably implemented to model a greater number of vehicle history attributes so that price values for these attributes can be calculated.

As explained above, step 74 results in a plurality of unsolved equations having a plurality of constants multiplied together with the corresponding values for the vehicle history attributes which are summed together to equal the determined price differential for the particular vehicle. The mathematically modeled equations are then simultaneously solved in step 76 to calculate the a, b, c, d, etc. constants which correspond to the price values for the vehicle history attributes. This may be attained using any appropriate mathematical techniques. However, in the illustrated implementation of the present invention, multivariate analysis is utilized to calculate the price values, such methods being known in mathematics.

The price values calculated in step 76 are stored in a database in step 78, for example, in price database 28 of FIG. 2. Of course, whereas the price values are shown as being calculated as a dollar amount in the illustrated example, the price values may alternatively be calculated as a percentage value, or in other appropriate manner.

In use, when a vehicle history report for a particular vehicle is requested in step 80, for instance, by a customer or user of the vehicle history information system 12 shown in FIG. 2, the vehicle history dataset associated with the particular vehicle is retrieved from the vehicle history database 30. The vehicle history attributes that are associated with the particular vehicle are identified and corresponding price values for the type of vehicle are retrieved from the price database 28 in step 82. It should be noted that the price values for the various vehicle history attributes shown in step 82 are merely examples only and are not actual values calculated using specific vehicle history datasets/transaction records.

The retrieved price values are then displayed in step 84 to allow the customer or user of the vehicle history information system 12 to determine the price of the particular vehicle in accordance with the vehicle history attributes. In this regard, the price values may be displayed to the user in conjunction with a vehicle history report generated by the vehicle history report module 35 which sets forth the vehicle history attributes of the particular vehicle which impacted its value. When another user requests a vehicle history report for another vehicle, the price values that are applicable to the vehicle history attributes for this vehicle can then be retrieved from the price database and provided to the user in a similar manner.

Of course, as also explained previously, other implementations of the present invention may include the step of obtaining a base price for the type of vehicle from a third party vehicle pricing services, and actually adjusting this base price using the price values for the corresponding vehicle history attributes. In addition, as previously noted, whereas the above described embodiment focused on the applicability of the present invention to automobiles, the present invention is also applicable to a variety of transportation devices including motorcycles, boats, airplanes, etc. Pricing values may be determined directly from each vehicle history attribute or vehicle history attributes can be bundled. For example, a rating or other indication of plural vehicle history attributes can be determined and a price may be determined based on the vehicle history attribute indication. The rating can be for specific vehicles, types of vehicles for all vehicles. As an example, any vehicle having a particular vehicle history rating can be discounted by a predetermined percentage.

Any vehicle history attributes can be used for a price determination, such as lag time between service records, type of seller, owner odometer patterns, distinctions between repairs, scheduled maintenance, and recommended maintenance, and ownership patterns (such as log of the number of owners and other mathematical patterns). A vehicle history attribute is any attribute of a vehicle that is indicative of past events to the vehicle as opposed to current condition of the vehicle.

It should further be evident to one of ordinary skill in the art that the present invention as described above may be readily implemented as a computer software that is loaded to run, for example, on a computer or other computing device. Thus, another aspect of the present invention is in providing a computer readable medium for generating price adjustments associated with vehicle history attributes. In one embodiment, the computer readable medium may comprise instructions for retrieving a plurality of vehicle history datasets relating to a plurality of vehicles, each dataset having vehicle history attributes regarding a particular vehicle, instructions for retrieving a plurality of transaction records relating to sale transactions of the plurality of vehicles, each transaction record including at least one of date and transaction price, instructions for analyzing whether sale transactions related to the plurality of transaction records occurred with knowledge by buyers of the plurality of vehicles as to vehicle history attributes associated with the plurality of vehicles, instructions for classifying the plurality of transaction records based on buyer's knowledge of the vehicle history attributes, and instructions for determining a price value for at least one of the vehicle history attributes based on the vehicle history datasets and classification of the plurality of transaction records.

In another embodiment, a computer readable medium for adjusting price of a used vehicle may comprise instructions for obtaining a plurality of vehicle history datasets relating to a plurality of vehicles, each of the datasets having vehicle history attributes regarding a particular vehicle, instructions for obtaining a plurality of transactional records relating to sale transactions of plurality of vehicles, each transaction record including at least one of date and transaction price, and instructions for determining price values for the vehicle history attributes based on the vehicle history datasets and the transaction records.

In still another embodiment, a computer readable medium for computing price values associated with vehicle history attributes may comprise instructions for obtaining a plurality of vehicle history datasets relating to a plurality of vehicles, each dataset having vehicle history attributes regarding a particular vehicle, instructions for determining a price differential between a price of a type of vehicle when plurality of vehicle history attributes associated with the type of vehicle are known to a vehicle buyer, and price of the type of vehicle when vehicle history attributes associated with the type of vehicle are not known to a vehicle buyer, and instructions for apportioning the price differential between the vehicle history attributes to generate the price values.

While various embodiments in accordance with the present invention have been shown and described, it is understood that the invention is not limited thereto. The present invention may be changed, modified and further applied by those skilled in the art. Therefore, this invention is not limited to the detail shown and described previously, but also includes all such changes and modifications. 

What is claimed:
 1. A method for obtaining at least one price value for a specific used vehicle based on vehicle history attributes of the specific used vehicle, said method comprising: receiving a vehicle identification indicating a specific used vehicle; receiving at least one vehicle history attribute indicator of the specific used vehicle; querying a pricing model based on said at least one vehicle history attribute of the specific vehicle, said pricing model being based on a plurality of vehicle history records related to a plurality of vehicles and having vehicle history attributes regarding said vehicles and a plurality of transaction records including a transaction price, said pricing model obtained by performing mathematical modeling on vehicle history attributes from said vehicle history records and transaction prices obtained from said transaction records to obtain price values corresponding to said at least one vehicle history attribute; and retrieving a price value for the specific vehicle from said pricing model based on said at least one vehicle history attribute indicator.
 2. The method as recited in claim 1, wherein the vehicle transaction records are related to transactions of said plurality of vehicles.
 3. The method as recited in claim 1, wherein each transaction record is related to a type of vehicle.
 4. The method as recited in claim 1, wherein the price value is a price adjustment to be applied to a base price of the vehicle.
 5. The method as recited in claim 1, wherein the price value is a vehicle price.
 6. The method as recited in claim 1, wherein the transaction prices are at least one of a survey price, a sales price, an offer price, and an auction price, or any combination thereof.
 7. The method as recited in claim 1, wherein said transaction records are survey records and said transaction prices are prices based on surveys.
 8. The method as recited in claim 1, wherein said mathematical model is created by performing multivariate analysis on the vehicle history attributes and the transaction prices.
 9. The method as recited in claim 1, wherein said mathematical model is created by a first stage of performing multivariate analysis on a collection of vehicle history attributes for various types of vehicles, and a second stage of performing multivariate analysis on a collection of vehicle history attributes for a specific type of vehicle.
 10. The method as recited in claim 8, wherein the multivariate analysis is regression analysis.
 11. A method as recited in claim 1, wherein said pricing model is further based on determining a likelihood of whether a purchaser had knowledge of relevant vehicle history attributes at a time of transactions corresponding to said transaction records, and performing mathematical modeling on the determined likelihood.
 12. The method as recited in claim 11, wherein said likelihood is a rating of a transaction in one of the categories of KNOWN, UNKNOWN, OR UNCERTAIN.
 13. The method as recited in claim 11, wherein the likelihood of whether a purchaser had knowledge is based on whether a vehicle history report for a particular vehicle corresponding to a transaction record was generated.
 14. The method as recited in claim 11, wherein the likelihood of whether a purchaser had knowledge is based on whether a vehicle history report for a particular vehicle corresponding to a transaction record was generated within a predetermined time frame of the corresponding transaction.
 15. A system for obtaining at least one price value for a specific used vehicle based on vehicle history attributes of the specific used vehicle, said system comprising: a vehicle history report module implemented on a computing device and configured to receive a vehicle identification indicating a specific used vehicle, said vehicle history report module also being configured to receive at least one vehicle history attribute indicator of the specific used vehicle; a user interface module implemented on hardware and configured to receive a query for a pricing model based on said at least one vehicle history attribute of the specific vehicle, said pricing model being based on a plurality of vehicle history records related to a plurality of vehicles and having vehicle history attributes regarding said vehicles and a plurality of transaction records including a transaction price, said pricing model obtained by performing mathematical modeling on vehicle history attributes from said vehicle history records and transaction prices obtained from said transaction records to obtain price values corresponding to said at least one vehicle history attribute; and a vehicle price adjustment module configured to retrieve a price value for the specific vehicle from said pricing model based on said at least one vehicle history attribute indicator.
 16. The system as recited in claim 15, wherein the vehicle transaction records are related to transactions of said plurality of vehicles.
 17. The system as recited in claim 15, wherein each transaction record is related to a type of vehicle.
 18. The system as recited in claim 15, wherein the price value is a price adjustment to be applied to a base price of the vehicle.
 19. The system as recited in claim 15, wherein the price value is a vehicle price.
 20. The system as recited in claim 15, wherein the transaction prices are at least one of a survey price, a sales price, an offer price, and an auction price, or any combination thereof.
 21. The system as recited in claim 15, wherein said transaction records are survey records and said transaction prices are prices based on surveys.
 22. The system as recited in claim 15, wherein said mathematical model is created by performing multivariate analysis on the vehicle history attributes and the transaction prices.
 23. The system as recited in claim 15, wherein said mathematical model is created by a first stage of performing multivariate analysis on a collection of vehicle history attributes for various types of vehicles, and a second stage of performing multivariate analysis on a collection of vehicle history attributes for a specific type of vehicle.
 24. The system as recited in claim 22, wherein the multivariate analysis is regression analysis.
 25. The system as recited in claim 15, wherein said pricing model is further based on determining a likelihood of whether a purchaser had knowledge of relevant vehicle history attributes at a time of transactions corresponding to said transaction records, and performing mathematical modeling on the determined likelihood.
 26. The system as recited in claim 25, wherein said likelihood is a rating of a transaction in one of the categories of KNOWN, UNKNOWN, OR UNCERTAIN.
 27. The system as recited in claim 25, wherein the likelihood of whether a purchaser had knowledge is based on whether a vehicle history report for a particular vehicle corresponding to a transaction record was generated.
 28. The system as recited in claim 25, wherein the likelihood of whether a purchaser had knowledge is based on whether a vehicle history report for a particular vehicle corresponding to a transaction record was generated within a predetermined time frame of the corresponding transaction. 